Adaptive Function Launching Acceleration in Serverless Computing Platforms - Xu, Zhengjun; Zhang, Haitao; Geng, Xin; Wu, Qiong; Ma, Huadong (2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), 2019)
Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services, which enables developers to focus more on business logic rather than on infrastructure. Serverless computing platform enables the function container scales to zero, which results in a serious problem called cold start. Cold start severely affects the responsiveness of serverless computing platform and limits the use and adoption of serverless computing by a broader range of applications. The traditional strategies reduce the cold start latency at the expense of resources. How to simultaneously minimize the cold start latency and reduce the resources consumption of strategy implementation is a challenging problem. In this paper, we firstly propose an Adaptive Warm-Up Strategy (AWUS) to predict the function invoking time and warm up the functions, thus reducing the cold start latency. We use the function chain model to improve the AWUS. We adopt a fine-grained regression method to predict non-first functions in the function chain more accurately. Secondly, we propose an Adaptive Container Pool Scaling Strategy (ACPSS) to reduce the function launching time. We dynamically adjust the capacity of the container pool to reduce the resources waste. The AWUS and ACPSS work together to reduce the cold start latency and the resources waste. Finally, we implement a serverless computing platform and conduct extensive experiments to evaluate our strategy. The evaluation results demonstrate the effectiveness of our strategies.